Science, marketing and wishful thinking in quantitative proteomics.
In a recent editorial (J. Proteome Res. 2007, 6, 1633) and elsewhere questions have been raised regarding the lack of attention paid to good analytical practice with respect to the reporting of quantitative results in proteomics. Using those comments as a starting point, several issues are discussed that relate to the challenges involved in achieving adequate sampling with MS-based methods in order to generate valid data for large-scale studies. The discussion touches on the relationships that connect sampling depth and the power to detect protein abundance change, conflict of interest, and strategies to overcome bureaucratic obstacles that impede the use of peer-to-peer technologies for transfer and storage of large data files generated in such experiments.
Hackett
Murray
M
Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA. mhackett@u.washington.edu
D002851:Chromatography, High Pressure Liquid / Q000379:methods; D030562:Databases, Protein / Q000639:trends; D007553:Isotope Labeling / Q000379:methods; D040541:Marketing; D013058:Mass Spectrometry / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D040081:Protein Array Analysis / Q000379:methods; D020543:Proteome; D040901:Proteomics / Q000379:methods* / Q000639:trends; D018401:Sample Size
D020543:Proteome